A decade ago, data brokers mostly collected and stored information. With time and the introduction of AI tools, information is easier to gather than ever. Be it a phone number, profile photo, or old public post, all can be linked across dozens of databases almost instantly.
In this article, we will look at how AI-powered tools are changing data brokers and privacy risks for everyday individuals. With regulation struggling to keep up, it’s important to understand what you can realistically do to keep your privacy.
How AI Changed What Data Brokers Can Do
Before AI tools became common, data brokers already collected vast amounts of data from various sources:
- Public records
- Marketing databases
- Social media platforms
- Property records
- Phone directories
- Legal paperwork
The only difference is that older systems had harder limits, matching records required manual checks or simple and limited automation. Because of this, results were fragmented and more often inaccurate. AI changed this in three major ways:
- Natural language processing allowed tools to pull information from unstructured text
- Computer vision lets tools match faces and images across platforms
- Entity resolution systems connect identities across various databases with much higher accuracy
All of this results in a very different data broker space compared to the last decade. A search once produced scattered results, but now creates a detailed identity profile in seconds. Systems are able to connect names, phone numbers, employment history, and other data. This is a serious cause for concern.
The Phone Number Problem in an AI-Powered Search Landscape
A phone number is a great example of a valuable digital key that data broker tools aggregate. Phone numbers have become deeply connected with everyone’s personal life. This is because they often hold most, if not all, MFA authentication and valuable applications used for banking or work.
Another problem with phone numbers being easily accessible is the presence of voice phishing attacks. These attacks use AI voice cloning combined with verified phone number data to create highly convincing scams. Each attack can be made very believable thanks to data brokers that compile profiles full of your data.
Because of this, it is important to know how to make your number private and identify AI-powered voice phishing. So make sure to think through calls, and remember that AI voice synthesis can imitate a trusted voice, but not their personality.
AI-Powered Facial Recognition and Public Image Data
Public photos create another major privacy issue, as your face is a great identification tool. Modern facial recognition systems can easily match faces across platforms and link professional accounts to personal ones. This is concerning, as any image creates a searchable identity trail that poses a risk to your privacy.
Luckily, the use of AI for these systems is deemed a “high-risk” and is placed under strict rules. They are a powerful tool in law enforcement and surveillance, but are slowly getting proper regulation.
Large Language Models and the Personal Data Extraction Risk
Source: Magnific
Large language models pose a privacy challenge as they are trained on huge amounts of scraped internet data. This includes datasets containing names, email addresses, posts, and other personal content that is or was publicly available.
This matters because it has been shown that LLMs can reproduce pieces of their training data. Effectively, this means that even if you delete a post or old profile, this does not remove it from an already-trained AI model’s dataset. This also creates a difficult legal question as to how the right to be forgotten applies to AI systems that learned from this data.
What the Regulatory Landscape Currently Covers – and What It Doesn’t
Current privacy laws still apply and are the foundation for modern privacy laws. All across the world, we are seeing rapid adaptation to modern AI tools and the many privacy concerns they bring. From biometric data or personal information, each country and act is added to regulate high-risk AI systems.
The biggest challenge is keeping up with modern innovations in the rapidly evolving sector. Most modern privacy laws were written before AI-scale data processing became common. As a result, regulators need to apply older concepts to newer AI systems while adapting to the scale and possibilities modern tech brings.
What This Means for Everyday Users Right Now
All of this affects all everyday users and impacts their privacy, regardless of how privacy-conscious they are. To see how much of your data is out there, you can simply search for your name on a reputable people-search tool. This will likely yield a surprising amount of data that will give you a realistic picture of your exposure.
Luckily, users still have some control, and you can take back some of your privacy by:
- Submitting data broker opt-out requests
- Removing publicly available data wherever possible
- Tightening social media privacy settings
- Using automated data removal tools
By doing all of this, you will significantly minimize your digital footprint and make it harder for data brokers to create profiles. Another great method to use is to have multiple email accounts. This will separate your data into multiple chunks that are harder to link to one another.
Where Responsibility Actually Lies
Ultimately, the responsibility lies with regulators who are creating current and future legal frameworks. The reality is clear: AI did not create the privacy problem; it just increased the speed, scale, and precision with which personal data can be aggregated and used. Regulators are slowly moving in the right direction, but there are still gaps.
As everyday users, it’s time to change how we control our personal data and understand the severity of this privacy problem. As AI-powered tools continue to evolve, our privacy will depend on conscious decisions and limiting unnecessary exposure. Make sure to take the time to stay informed and change how you handle your personal data.
